def test_nich_hp_sigmasq():
N = 1000
Y = np.array([(x, ) for x in np.random.uniform(low=-1, high=1, size=N)],
dtype=[('', np.float32)])
view = numpy_dataview(Y)
grid_min, grid_max, grid_n = 0.0001, 1.0, 100
_test_scalar_hp_inference(view, log_exponential(1.), 0.1, grid_min,
grid_max, grid_n, nich, 'sigmasq')
def test_log_exponential():
from microscopes.common.scalar_functions import log_exponential
import math
lam = 2.
fn = log_exponential(lam)
x = 10.
assert_almost_equals(math.log(lam * math.exp(-lam * x)), fn(x), places=5)
assert math.isinf(fn(-10.))
def test_log_exponential():
from microscopes.common.scalar_functions import log_exponential
import math
lam = 2.
fn = log_exponential(lam)
x = 10.
assert_almost_equals(math.log(lam * math.exp(-lam * x)), fn(x), places=5)
assert math.isinf(fn(-10.))
def test_gp_hp_inv_beta():
N = 1000
Y = np.array([(x, ) for x in np.random.randint(low=0, high=10, size=N)],
dtype=[('', np.bool)])
view = numpy_dataview(Y)
grid_min, grid_max, grid_n = 0.001, 2.0, 100
_test_scalar_hp_inference(view, log_exponential(1.), 0.1, grid_min,
grid_max, grid_n, gp, 'inv_beta')
def test_bnb_hp_alpha():
N = 1000
Y = np.array([(x, ) for x in np.random.randint(low=0, high=10, size=N)],
dtype=[('', np.bool)])
view = numpy_dataview(Y)
grid_min, grid_max, grid_n = 0.01, 5.0, 100
_test_scalar_hp_inference(view, log_exponential(1.), 1., grid_min,
grid_max, grid_n, bnb, 'alpha')
def test_nich_hp_sigmasq():
N = 1000
Y = np.array([(x,) for x in np.random.uniform(low=-1, high=1, size=N)],
dtype=[('', np.float32)])
view = numpy_dataview(Y)
grid_min, grid_max, grid_n = 0.0001, 1.0, 100
_test_scalar_hp_inference(view,
log_exponential(1.),
0.1,
grid_min,
grid_max,
grid_n,
nich,
'sigmasq')
def test_gp_hp_inv_beta():
N = 1000
Y = np.array([(x,) for x in np.random.randint(low=0, high=10, size=N)],
dtype=[('', np.bool)])
view = numpy_dataview(Y)
grid_min, grid_max, grid_n = 0.001, 2.0, 100
_test_scalar_hp_inference(view,
log_exponential(1.),
0.1,
grid_min,
grid_max,
grid_n,
gp,
'inv_beta')
def test_bnb_hp_alpha():
N = 1000
Y = np.array([(x,) for x in np.random.randint(low=0, high=10, size=N)],
dtype=[('', np.bool)])
view = numpy_dataview(Y)
grid_min, grid_max, grid_n = 0.01, 5.0, 100
_test_scalar_hp_inference(view,
log_exponential(1.),
1.,
grid_min,
grid_max,
grid_n,
bnb,
'alpha')
def test_kernel_slice_cluster_hp():
prior_fn = log_exponential(1.5)
def init_inf_kernel_state_fn(s):
cparam = {'alpha': (prior_fn, 1.)}
return cparam
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, cparam=arg)
grid_min, grid_max, grid_n = 0.0, 50., 100
_test_cluster_hp_inference(initialize,
prior_fn,
grid_min,
grid_max,
grid_n,
numpy_dataview,
bind,
init_inf_kernel_state_fn,
kernel_fn,
map_actual_postprocess_fn=lambda x: x,
prng=rng())
def test_kernel_slice_hp():
indiv_prior_fn = log_exponential(1.2)
def init_inf_kernel_state_fn(s):
hparams = {
0: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
}
}
return hparams
def prior_fn(raw):
return indiv_prior_fn(raw['alpha']) + indiv_prior_fn(raw['beta'])
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, hparams=arg)
_test_kernel_slice_hp(initialize, init_inf_kernel_state_fn, prior_fn,
numpy_dataview, bind, kernel_fn,
'grid_slice_hp_samples.pdf', rng())
def test_kernel_slice_cluster_hp():
prior_fn = log_exponential(1.5)
def init_inf_kernel_state_fn(s):
cparam = {'alpha': (prior_fn, 1.)}
return cparam
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, cparam=arg)
grid_min, grid_max, grid_n = 0.0, 50., 100
_test_cluster_hp_inference(initialize,
prior_fn,
grid_min,
grid_max,
grid_n,
numpy_dataview,
bind,
init_inf_kernel_state_fn,
kernel_fn,
map_actual_postprocess_fn=lambda x: x,
prng=rng())
def test_kernel_slice_hp():
indiv_prior_fn = log_exponential(1.2)
def init_inf_kernel_state_fn(s):
hparams = {
0: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
}
}
return hparams
def prior_fn(raw):
return indiv_prior_fn(raw['alpha']) + indiv_prior_fn(raw['beta'])
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, hparams=arg)
_test_kernel_slice_hp(initialize,
init_inf_kernel_state_fn,
prior_fn,
numpy_dataview,
bind,
kernel_fn,
'grid_slice_hp_samples.pdf',
rng())
def test_mnist_supervised():
mnist_dataset = _get_mnist_dataset()
classes = range(10)
classmap = {c: i for i, c in enumerate(classes)}
train_data, test_data = [], []
for c in classes:
Y = mnist_dataset['data'][
np.where(mnist_dataset['target'] == float(c))[0]]
Y_train, Y_test = train_test_split(Y, test_size=0.01)
train_data.append(Y_train)
test_data.append(Y_test)
sample_size_max = 10000
def mk_class_data(c, Y):
n, D = Y.shape
print 'number of digit', c, 'in training is', n
dtype = [('', bool)] * D + [('', int)]
inds = np.random.permutation(Y.shape[0])[:sample_size_max]
Y = np.array([tuple(list(y) + [classmap[c]]) for y in Y[inds]],
dtype=dtype)
return Y
Y_train = np.hstack([mk_class_data(c, y)
for c, y in zip(classes, train_data)])
Y_train = Y_train[np.random.permutation(np.arange(Y_train.shape[0]))]
n, = Y_train.shape
D = len(Y_train.dtype)
print 'training data is', n, 'examples'
print 'image dimension is', (D - 1), 'pixels'
view = numpy_dataview(Y_train)
defn = model_definition(n, [bb] * (D - 1) + [dd(len(classes))])
r = rng()
s = initialize(defn,
view,
cluster_hp={'alpha': 0.2},
feature_hps=[{'alpha': 1., 'beta': 1.}] *
(D - 1) + [{'alphas': [1. for _ in classes]}],
r=r)
bound_s = bind(s, view)
indiv_prior_fn = log_exponential(1.2)
hparams = {
i: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
} for i in xrange(D - 1)}
hparams[D - 1] = {
'alphas[{}]'.format(idx): (indiv_prior_fn, 1.5)
for idx in xrange(len(classes))
}
def print_prediction_results():
results = []
for c, Y_test in zip(classes, test_data):
for y in Y_test:
query = ma.masked_array(
np.array([tuple(y) + (0,)],
dtype=[('', bool)] * (D - 1) + [('', int)]),
mask=[(False,) * (D - 1) + (True,)])[0]
samples = [
s.sample_post_pred(query, r)[1][0][-1] for _ in xrange(30)]
samples = np.bincount(samples, minlength=len(classes))
prediction = np.argmax(samples)
results.append((classmap[c], prediction, samples))
print 'finished predictions for class', c
Y_actual = np.array([a for a, _, _ in results], dtype=np.int)
Y_pred = np.array([b for _, b, _ in results], dtype=np.int)
print 'accuracy:', accuracy_score(Y_actual, Y_pred)
print 'confusion matrix:'
print confusion_matrix(Y_actual, Y_pred)
# AUROC for one vs all (each class)
for i, clabel in enumerate(classes):
Y_true = np.copy(Y_actual)
# treat class c as the "positive" example
positive_examples = Y_actual == i
negative_examples = Y_actual != i
Y_true[positive_examples] = 1
Y_true[negative_examples] = 0
Y_prob = np.array([float(c[i]) / c.sum() for _, _, c in results])
cls_auc = roc_auc_score(Y_true, Y_prob)
print 'class', clabel, 'auc=', cls_auc
#import matplotlib.pylab as plt
#Y_prob = np.array([c for _, _, c in results])
#fpr, tpr, thresholds = roc_curve(Y_actual, Y_prob, pos_label=0)
#plt.plot(fpr, tpr)
#plt.show()
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
# print group size breakdown
sizes = [(gid, s.groupsize(gid)) for gid in s.groups()]
sizes = sorted(sizes, key=lambda x: x[1], reverse=True)
print ' group_sizes=', sizes
print_prediction_results()
# save state
mkdirp("mnist-states")
fname = os.path.join("mnist-states", "state-iter{}.ser".format(rid))
with open(fname, "w") as fp:
fp.write(s.serialize())
# training
iters = 30
for rid in xrange(iters):
kernel(rid)
def test_mnist():
import matplotlib.pylab as plt
from PIL import Image, ImageOps
mnist_dataset = _get_mnist_dataset()
Y_2 = mnist_dataset['data'][np.where(mnist_dataset['target'] == 2.)[0]]
Y_3 = mnist_dataset['data'][np.where(mnist_dataset['target'] == 3.)[0]]
print 'number of twos:', Y_2.shape[0]
print 'number of threes:', Y_3.shape[0]
_, D = Y_2.shape
W = int(math.sqrt(D))
assert W * W == D
dtype = [('', bool)] * D
Y = np.vstack([Y_2, Y_3])
Y = np.array(
[tuple(y) for y in Y[np.random.permutation(np.arange(Y.shape[0]))]],
dtype=dtype)
view = numpy_dataview(Y)
defn = model_definition(Y.shape[0], [bb] * D)
r = rng()
s = initialize(
defn,
view,
cluster_hp={'alpha': 0.2},
feature_hps=[{'alpha': 1., 'beta': 1.}] * D,
r=r)
bound_s = bind(s, view)
indiv_prior_fn = log_exponential(1.2)
hparams = {
i: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
} for i in xrange(D)}
def plot_clusters(s, fname, scalebysize=False):
hps = [s.get_feature_hp(i) for i in xrange(D)]
def prior_prob(hp):
return hp['alpha'] / (hp['alpha'] + hp['beta'])
def data_for_group(gid):
suffstats = [s.get_suffstats(gid, i) for i in xrange(D)]
def prob(hp, ss):
top = hp['alpha'] + ss['heads']
bot = top + hp['beta'] + ss['tails']
return top / bot
probs = [prob(hp, ss) for hp, ss in zip(hps, suffstats)]
return np.array(probs)
def scale(d, weight):
im = d.reshape((W, W))
newW = max(int(weight * W), 1)
im = Image.fromarray(im)
im = im.resize((newW, newW))
im = ImageOps.expand(im, border=(W - newW) / 2)
im = np.array(im)
a, b = im.shape
#print 'a,b:', a, b
if a < W:
im = np.append(im, np.zeros(b)[np.newaxis, :], axis=0)
elif a > W:
im = im[:W, :]
assert im.shape[0] == W
if b < W:
#print 'current:', im.shape
im = np.append(im, np.zeros(W)[:, np.newaxis], axis=1)
elif b > W:
im = im[:, :W]
assert im.shape[1] == W
return im.flatten()
data = [(data_for_group(g), cnt) for g, cnt in groupsbysize(s)]
largest = max(cnt for _, cnt in data)
data = [scale(d, cnt / float(largest)) if scalebysize else d
for d, cnt in data]
digits_per_row = 12
rem = len(data) % digits_per_row
if rem:
fill = digits_per_row - rem
for _ in xrange(fill):
data.append(np.zeros(D))
assert not (len(data) % digits_per_row)
#rows = len(data) / digits_per_row
data = np.vstack([np.hstack([d.reshape((W, W))
for d in data[i:i + digits_per_row]])
for i in xrange(0, len(data), digits_per_row)])
#print 'saving figure', fname
plt.imshow(data, cmap=plt.cm.binary, interpolation='nearest')
plt.savefig(fname)
plt.close()
def plot_hyperparams(s, fname):
hps = [s.get_feature_hp(i) for i in xrange(D)]
alphas = np.array([hp['alpha'] for hp in hps])
betas = np.array([hp['beta'] for hp in hps])
data = np.hstack([alphas.reshape((W, W)), betas.reshape((W, W))])
plt.imshow(data, interpolation='nearest')
plt.colorbar()
plt.savefig(fname)
plt.close()
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
return s.score_joint(r)
# burnin
burnin = 20
for rid in xrange(burnin):
print 'score:', kernel(rid)
print 'finished burnin'
plot_clusters(s, 'mnist_clusters.pdf')
plot_clusters(s, 'mnist_clusters_bysize.pdf', scalebysize=True)
plot_hyperparams(s, 'mnist_hyperparams.pdf')
print 'groupcounts:', groupcounts(s)
# posterior predictions
present = D / 2
absent = D - present
queries = [tuple(Y_2[i]) for i in np.random.permutation(Y_2.shape[0])[:4]] + \
[tuple(Y_3[i]) for i in np.random.permutation(Y_3.shape[0])[:4]]
queries_masked = ma.masked_array(
np.array(queries, dtype=[('', bool)] * D),
mask=[(False,) * present + (True,) * absent])
def postpred_sample(y_new):
Y_samples = [s.sample_post_pred(y_new, r)[1] for _ in xrange(1000)]
Y_samples = np.array([list(y) for y in np.hstack(Y_samples)])
Y_avg = Y_samples.mean(axis=0)
return Y_avg
queries_masked = [postpred_sample(y) for y in queries_masked]
data0 = np.hstack([q.reshape((W, W)) for q in queries_masked])
data1 = np.hstack(
[np.clip(np.array(q, dtype=np.float), 0., 1.).reshape((W, W))
for q in queries])
data = np.vstack([data0, data1])
plt.imshow(data, cmap=plt.cm.binary, interpolation='nearest')
plt.savefig('mnist_predict.pdf')
plt.close()
def test_mnist_supervised(n):
mnist_dataset = _get_mnist_dataset()
classes = range(10)
classmap = {c: i for i, c in enumerate(classes)}
train_data, test_data = [], []
for c in classes:
Y = mnist_dataset['data'][np.where(
mnist_dataset['target'] == float(c))[0]]
Y_train, Y_test = train_test_split(Y, test_size=0.01)
train_data.append(Y_train)
test_data.append(Y_test)
sample_size_max = n
def mk_class_data(c, Y):
n, D = Y.shape
print 'number of digit', c, 'in training is', n
dtype = [('', bool)] * D + [('', int)]
inds = np.random.permutation(Y.shape[0])[:sample_size_max]
Y = np.array([tuple(list(y) + [classmap[c]]) for y in Y[inds]],
dtype=dtype)
return Y
Y_train = np.hstack(
[mk_class_data(c, y) for c, y in zip(classes, train_data)])
Y_train = Y_train[np.random.permutation(np.arange(Y_train.shape[0]))]
n, = Y_train.shape
D = len(Y_train.dtype)
print 'training data is', n, 'examples'
print 'image dimension is', (D - 1), 'pixels'
view = numpy_dataview(Y_train)
defn = model_definition(n, [bb] * (D - 1) + [dd(len(classes))])
r = rng()
s = initialize(defn,
view,
cluster_hp={'alpha': 0.2},
feature_hps=[{
'alpha': 1.,
'beta': 1.
}] * (D - 1) + [{
'alphas': [1. for _ in classes]
}],
r=r)
bound_s = bind(s, view)
indiv_prior_fn = log_exponential(1.2)
hparams = {
i: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
}
for i in xrange(D - 1)
}
hparams[D - 1] = {
'alphas[{}]'.format(idx): (indiv_prior_fn, 1.5)
for idx in xrange(len(classes))
}
def print_prediction_results():
results = []
for c, Y_test in zip(classes, test_data):
for y in Y_test:
query = ma.masked_array(
np.array([tuple(y) + (0, )],
dtype=[('', bool)] * (D - 1) + [('', int)]),
mask=[(False, ) * (D - 1) + (True, )])[0]
samples = [
s.sample_post_pred(query, r)[1][0][-1] for _ in xrange(30)
]
samples = np.bincount(samples, minlength=len(classes))
prediction = np.argmax(samples)
results.append((classmap[c], prediction, samples))
print 'finished predictions for class', c
Y_actual = np.array([a for a, _, _ in results], dtype=np.int)
Y_pred = np.array([b for _, b, _ in results], dtype=np.int)
print 'accuracy:', accuracy_score(Y_actual, Y_pred)
print 'confusion matrix:'
print confusion_matrix(Y_actual, Y_pred)
# AUROC for one vs all (each class)
for i, clabel in enumerate(classes):
Y_true = np.copy(Y_actual)
# treat class c as the "positive" example
positive_examples = Y_actual == i
negative_examples = Y_actual != i
Y_true[positive_examples] = 1
Y_true[negative_examples] = 0
Y_prob = np.array([float(c[i]) / c.sum() for _, _, c in results])
cls_auc = roc_auc_score(Y_true, Y_prob)
print 'class', clabel, 'auc=', cls_auc
#import matplotlib.pylab as plt
#Y_prob = np.array([c for _, _, c in results])
#fpr, tpr, thresholds = roc_curve(Y_actual, Y_prob, pos_label=0)
#plt.plot(fpr, tpr)
#plt.show()
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
# training
iters = 30
for rid in xrange(iters):
kernel(rid)
# print group size breakdown
sizes = [(gid, s.groupsize(gid)) for gid in s.groups()]
sizes = sorted(sizes, key=lambda x: x[1], reverse=True)
print ' group_sizes=', sizes
#print_prediction_results()
# save state
mkdirp("mnist-states")
fname = os.path.join("mnist-states", "state-iter{}.ser".format(rid))
with open(fname, "w") as fp:
fp.write(s.serialize())
def test_mnist():
import matplotlib.pylab as plt
from PIL import Image, ImageOps
mnist_dataset = _get_mnist_dataset()
Y_2 = mnist_dataset['data'][np.where(mnist_dataset['target'] == 2.)[0]]
Y_3 = mnist_dataset['data'][np.where(mnist_dataset['target'] == 3.)[0]]
print 'number of twos:', Y_2.shape[0]
print 'number of threes:', Y_3.shape[0]
_, D = Y_2.shape
W = int(math.sqrt(D))
assert W * W == D
dtype = [('', bool)] * D
Y = np.vstack([Y_2, Y_3])
Y = np.array(
[tuple(y) for y in Y[np.random.permutation(np.arange(Y.shape[0]))]],
dtype=dtype)
view = numpy_dataview(Y)
defn = model_definition(Y.shape[0], [bb] * D)
r = rng()
s = initialize(defn,
view,
cluster_hp={'alpha': 0.2},
feature_hps=[{
'alpha': 1.,
'beta': 1.
}] * D,
r=r)
bound_s = bind(s, view)
indiv_prior_fn = log_exponential(1.2)
hparams = {
i: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
}
for i in xrange(D)
}
def plot_clusters(s, fname, scalebysize=False):
hps = [s.get_feature_hp(i) for i in xrange(D)]
def prior_prob(hp):
return hp['alpha'] / (hp['alpha'] + hp['beta'])
def data_for_group(gid):
suffstats = [s.get_suffstats(gid, i) for i in xrange(D)]
def prob(hp, ss):
top = hp['alpha'] + ss['heads']
bot = top + hp['beta'] + ss['tails']
return top / bot
probs = [prob(hp, ss) for hp, ss in zip(hps, suffstats)]
return np.array(probs)
def scale(d, weight):
im = d.reshape((W, W))
newW = max(int(weight * W), 1)
im = Image.fromarray(im)
im = im.resize((newW, newW))
im = ImageOps.expand(im, border=(W - newW) / 2)
im = np.array(im)
a, b = im.shape
#print 'a,b:', a, b
if a < W:
im = np.append(im, np.zeros(b)[np.newaxis, :], axis=0)
elif a > W:
im = im[:W, :]
assert im.shape[0] == W
if b < W:
#print 'current:', im.shape
im = np.append(im, np.zeros(W)[:, np.newaxis], axis=1)
elif b > W:
im = im[:, :W]
assert im.shape[1] == W
return im.flatten()
data = [(data_for_group(g), cnt) for g, cnt in groupsbysize(s)]
largest = max(cnt for _, cnt in data)
data = [
scale(d, cnt / float(largest)) if scalebysize else d
for d, cnt in data
]
digits_per_row = 12
rem = len(data) % digits_per_row
if rem:
fill = digits_per_row - rem
for _ in xrange(fill):
data.append(np.zeros(D))
assert not (len(data) % digits_per_row)
#rows = len(data) / digits_per_row
data = np.vstack([
np.hstack([d.reshape((W, W)) for d in data[i:i + digits_per_row]])
for i in xrange(0, len(data), digits_per_row)
])
#print 'saving figure', fname
plt.imshow(data, cmap=plt.cm.binary, interpolation='nearest')
plt.savefig(fname)
plt.close()
def plot_hyperparams(s, fname):
hps = [s.get_feature_hp(i) for i in xrange(D)]
alphas = np.array([hp['alpha'] for hp in hps])
betas = np.array([hp['beta'] for hp in hps])
data = np.hstack([alphas.reshape((W, W)), betas.reshape((W, W))])
plt.imshow(data, interpolation='nearest')
plt.colorbar()
plt.savefig(fname)
plt.close()
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
return s.score_joint(r)
# burnin
burnin = 20
for rid in xrange(burnin):
print 'score:', kernel(rid)
print 'finished burnin'
plot_clusters(s, 'mnist_clusters.pdf')
plot_clusters(s, 'mnist_clusters_bysize.pdf', scalebysize=True)
plot_hyperparams(s, 'mnist_hyperparams.pdf')
print 'groupcounts:', groupcounts(s)
# posterior predictions
present = D / 2
absent = D - present
queries = [tuple(Y_2[i]) for i in np.random.permutation(Y_2.shape[0])[:4]] + \
[tuple(Y_3[i]) for i in np.random.permutation(Y_3.shape[0])[:4]]
queries_masked = ma.masked_array(np.array(queries, dtype=[('', bool)] * D),
mask=[(False, ) * present +
(True, ) * absent])
def postpred_sample(y_new):
Y_samples = [s.sample_post_pred(y_new, r)[1] for _ in xrange(1000)]
Y_samples = np.array([list(y) for y in np.hstack(Y_samples)])
Y_avg = Y_samples.mean(axis=0)
return Y_avg
queries_masked = [postpred_sample(y) for y in queries_masked]
data0 = np.hstack([q.reshape((W, W)) for q in queries_masked])
data1 = np.hstack([
np.clip(np.array(q, dtype=np.float), 0., 1.).reshape((W, W))
for q in queries
])
data = np.vstack([data0, data1])
plt.imshow(data, cmap=plt.cm.binary, interpolation='nearest')
plt.savefig('mnist_predict.pdf')
plt.close()
